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1.
Abstract

Fast screening methods are needed for plant breeding. The objective of this research was to evaluate the potential of near‐infrared reflectance spectroscopy (NIRS) for the simultaneous analysis of dry matter and protein contents in intact discs of fresh yam bean (Pachyrhizus spp.) tubers. Discs from 210 tubers were extracted with a punch few hours after harvesting and scanned by NIRS using a specially designed adapter. External validation revealed a close relationship between NIRS and reference methods for dry matter content (r2=0.94; standard error of performance, SEP=1.2%) and protein content (r2=0.87; SEP=1.94%). The calibration for protein content was compared with another one developed using dried‐ground tuber samples (r2=0.97; SEP=0.97%). These results suggested that NIRS can be used to determine dry matter and protein contents in fresh tuber samples of yam beans with acceptable accuracy. Further research will have to determine if additional traits can be incorporated into this scheme.  相似文献   

2.
In order to provide references for leaf nutrition diagnosis of fingered citron, the technique of near infrared reflectance spectroscopy (NIRS) was introduced to analyze nitrogen (N), phosphorus (P), potassium (K), iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) in the dry-leaf samples of fingered citron. The best calibration model for N was developed with high RSQCAL (0.90), SD/SECV (2.73) and low SEC (1.06 mg g?1), good calibration models were obtained for P, K, Fe and Mn, and no significant correlations were found between the spectra and the individual amounts of Zn and Cu. When tested using a validation set (n = 38), N was well predicted with low values of SEP (1.21 mg g?1) and high RPD (2.5). The values of SEP and RPD were also acceptable for the external validation of P, Fe and Mn. Near-infrared spectroscopy analysis technique shows potential of diagnosing minerals in fingered citron, particularly for N, P, Fe and Mn.  相似文献   

3.
近红外光谱法测定玉米秸秆饲用品质   总被引：6，自引：1，他引：5  
为了对玉米秸秆的饲用品质进行可靠、便捷、快速的分析和评价,该研究以不同品种、密度、氮肥和水分处理的不同发育时期和不同部位玉米秸秆为试验材料,应用近红外光谱（NIRS）技术和偏最小二乘法（PLS）,采用一阶导数+中心化+多元散射校正的光谱数据预处理方法,构建了玉米秸秆体外干物质消化率（IVDMD）、酸性洗涤纤维（ADF）、中性洗涤纤维（NDF） 和可溶性糖（WSC）含量的NIRS分析模型。所建立的IVDMD、ADF、NDF和WSC含量的NIRS校正模型决定系数（R2cal）分别为0.9906、0.9870、0.9931和0.9802,交叉验证决定系数（R2cv）分别为0.9593、0.9413 、0.9678和0.9342,外部验证决定系数（R2val）分别为0.9549、0.9353、0.9519和0.9191,各项标准差（SEC、SECV和SEP）为0.935～1.904,相对分析误差（RPD）均大于3。结果表明,各参数的NIRS分析模型可用于玉米秸秆饲用品质的分析和品种选育的快速鉴定。  相似文献   

4.
Detection of sodium chloride in cured salmon roe by SW-NIR spectroscopy   总被引：4，自引：0，他引：4  
Salt and moisture content of cured salmon roe (ikura) was determined using short-wavelength-near-infrared (SW-NIR) reflectance spectroscopy (600-1100 nm). SW-NIR can be used to measure chloride species. Calibrations for salt in bulk samples of high-quality roe (R(2) = 0.904, SEP = 0.421%, RPD = 3.21) and average-quality roe (R(2) = 0.711, SEP = 1.13%, RPD = 1.81), crushed eggs (R(2) = 0.857, SEP = 0.509%, RPD = 2.62), and individual eggs (R(2) = 0.731, SEP = 0.698%, RPD = 1.98) were developed using partial least squares (PLS) regression models. The heterogeneous distribution of lipid and moisture in the individual eggs limit the sensitivity of this method; however, this method provides a rapid nondestructive method for high-value food products where destructive testing is expensive or impractical and for process control applications.  相似文献   

5.
The effect of drying conditions on harpagoside (HS) retention, as well as the use of near-infrared spectroscopy (NIRS) for rapid quantification of the iridoids, HS, and 8-rho-coumaroyl harpagide (8rhoCHG) and moisture, in dried Harpagophytum procumbens (devil's claw) root was investigated. HS retention was significantly (P < 0.05) lower in sun-dried samples as compared to tunnel-dried (60 degrees C, 30% relative humidity) and freeze-dried samples. The best retention of HS was obtained at 50 degrees C when evaluating tunnel drying at dry bulb temperatures of 40, 50, and 60 degrees C and 30% relative humidity. NIRS can effectively predict moisture content with a standard error of prediction (SEP) and correlation coefficient (r) of 0.24% and 0.99, respectively. The HS and 8rhoCHG NIRS calibration models established for both iridoid glucosides can be used for screening purposes to get a semiquantitative classification of devil's claw roots (for HS: SEP = 0.236%, r = 0.64; for 8rhoCHG: SEP = 0.048%, r = 0.73).  相似文献   

6.
A quick method was developed for diagnosis of nitrogen (N) in apple trees based on multiple linear regressions to establish the relationship between near-infrared reflectance spectra (NIRS) and the N contents of fresh and dry tissue. Spectral pretreatment methods such as derivatives, smoothing, and normalization were used. The derivatives appeared to be the most effective. The best calibration for fresh leaf gave 0.842 for the correlation coefficient of validation (Rv), 1.119 g kg?1 for the root mean square error of prediction (RMSEP), and 8.311 for the ratio of the range in reference data from the validation samples to the root mean square error of prediction (RER). The best calibration for dried ground samples was obtained with Rv = 0.952, RMSEP = 0.633 g kg?1, the ratio performance deviation (RPD) = 3.27, and RER = 13.728. The results showed that calibrations of dry-apple leaf are robust enough for an accurate prediction of N.  相似文献   

7.
Sesame (Sesamum indicum L.) contains abundant lignans including lipid-soluble lignans (sesamin and sesamolin) and water-soluble lignan glycosides (sesaminol triglucoside and sesaminol diglucoside) related to antioxidative activity. In this study, near infrared reflectance spectroscopy (NIRS) was used to develop a rapid and nondestructive method for the determination of lignan contents on intact sesame seeds. Ninety-three intact seeds were scanned in the reflectance mode of a scanning monochromator. This scanning procedure did not require the pulverization of samples, allowing each analysis to be completed within minutes. Reference values for lignan contents were obtained by high-performance liquid chromatography analysis. Calibration equations for lignans (sesamin and sesamolin) and lignan glycosides (sesaminol triglucoside and sesaminol diglucoside) contents were developed using modified partial least squares regression with internal cross-validation (n = 63). The equations obtained had low standard errors of cross-validation and moderate R2 (coefficient of determination in calibration). The prediction of an external validation set (n = 30) showed significant correlation between reference values and NIRS predicted values based on the SEP (standard error of prediction), bias, and r2 (coefficient of determination in prediction). The models developed in this study had relatively higher values (more than 2.0) of SD/SEP(C) for all lignans and lignan glycosides except for sesaminol diglucoside, which had a minor amount, indicating good correlation between the reference and the NIRS estimate. The results showed that NIRS, a nondestructive screening method, could be used to rapidly determine lignan and lignan glycoside contents in the breeding programs for high quality sesame.  相似文献   

8.
A rapid predictive method based on near-infrared spectroscopy (NIRS) was developed to measure acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) of rice stem materials. A total of 207 samples were divided into two subsets, one subset (approximately 136 samples) for calibration and cross-validation and the other subset for independent external validation to evaluate the calibration equations. Different mathematical treatments were applied to obtain the best calibration and validation results. The highest coefficient of determination for calibration (R2) and coefficient of determination for cross-validation (1-VR) were 0.968 and 0.949 for ADF, 0.846 and 0.812 for NDF, and 0.897 and 0.843 for ADL, respectively. Independent external validation still gave a high coefficient of determination for external validation (r2) and a low standard error of performance (SEP) for the three parameters; the best validation results were SEP = 0.933 and r2 = 0.959 for ADF, SEP = 2.228 and r2 = 0.775 for NDF, and SEP = 0.616 and r2 = 0.847 for ADL, indicating that NIR gave a sufficiently accurate prediction of ADF and ADL content of rice material but a less satisfactory prediction for NDF. This study suggested that routine screening for these forage quality parameters with large numbers of samples is possible with NIRS in early-generation selection in rice-breeding programs.  相似文献   

9.
A study was conducted to investigate methods of improving a near-infrared transmittance spectroscopy (NITS) amylose calibration that could serve as a rapid, nondestructive alternative to traditional methods for determining amylose content in corn. Calibrations were developed using a set of genotypes possessing endosperm mutations in single- and double-mutant combinations ranging in starch-amylose content (SAC) from -8.5 to 76%, relative to a standard curve. The influence of three factors were examined including comparing calibrations made against SAC versus grain amylose content (GAC), developing calibrations using partial least squares (PLS) analysis versus artificial neural networking (ANN), and using all samples in the calibrations set versus using progressively narrower ranges of SAC or GAC in the calibration set. Grain samples were divided into calibration and validation sets for PLS analysis while samples used in ANN were assigned to a training set, test set, and validation set. Performance statistics of the validation sets that were considered were the coefficient of determination (R), the standard error of prediction (SEP), and the ratio of the standard deviation of amylose values to the SEP (RPD), which were used to compare all NITS models. The study revealed an NITS prediction model for SAC (R = 0.96, SEP = 5.1%, RDP = 3.8) of similar precision to the best GAC model (R = 0.96, SEP = 2.7%, RPD = 3.5). Narrowing the amylose range of the calibration set generally did not improve performance statistics except for PLS models for SAC in which a decrease in SEP values was observed. In one model, the SEP improved while R and RPD remained constant (R = 0.94, SEP = 4.2%, RPD = 2.8) when samples with SAC values <20% were removed from the calibration set. Although the NITS amylose calibrations in this study are of limited precision, they may be useful when a rough screening method is needed for SAC. For example, NITS may be useful to detect severe contamination during transport and storage of specialty grains or to aid breeders when selecting for amylose content from large numbers of grain samples.  相似文献   

10.
High cost and painstaking procedures associated with fatty acid analyses of maize kernel necessitate the use of alternative methods. NIR spectroscopy offers advantages in this respect for a variety of areas such as plant breeding, food and feed industries, and biofuel production, in which different forms of maize kernel (e.g., intact kernel, flour, or oil) are used as material. We investigated the possibility of estimating maize oil quality traits by using different samples (intact kernel, flour, and oil) and conventional regression methods (multiple linear regression [MLR] and partial least squares regression [PLSR]) applied to their NIR spectra. MLR and PLSR calibration models were developed for oleic acid, linoleic acid, oleic/linoleic acid ratios, total monounsaturated fatty acid, total polyunsaturated fatty acid (PUFA), and total saturated fatty acid by analyzing 120 maize samples. Robustness in terms of prediction accuracy of the models developed here was tested with a reserved set of samples (n = 30). The results suggested that fatty acids could be possibly estimated by calibrations developed from flour and oil samples with a high degree of accuracy, whereas intact samples did not offer satisfactory results. PLSR and MLR methods gave better results in flour and oil samples, respectively. PUFA was the trait that was most successfully estimated from both flour (for the PLSR model, standard error of the estimate [SEP] of 1.78%, relative performance to deviation [RPD] of 3.09, R2 = 0.93) and oil (for the MLR model, SEP of 0.85%, RPD of 6.52, R2 = 0.98) samples. We concluded that sample type and chemometric method should be handled as important factors in calibration development, and the effects of these factors may vary depending on the trait being analyzed.  相似文献   

11.
The legal method (polarimetric measurement) for the determination of sucrose content and the wet chemical analysis for the quality control of sugar beet uses lead acetate. Because heavy metals are pollutants, the law could forbid their use in the future. Therefore, near-infrared spectroscopy (NIRS) was evaluated as a procedure to replace these methods. However, there are alternatives to lead clarification, such as the use of aluminum salts, which have been applied at many sugar companies. The real advantage of NIRS is in speed and ease of analysis. The aim of this study was to determine simultaneously the concentration of several components which define the industrial quality of beets. The first objective was the determination of sucrose content, which determines the sugar beet price. The standard error of prediction (SEP) was low: 0.11 g of sucrose/100 g of fresh beet. NIRS was also able to determine other beet quality parameters: brix, marc, glucose, nitrogen, sodium, potassium, sugar in molasses (i.e. sucrose in molasses), and juice purity. The results concerning brix, marc, sugar in molasses, and juice purity were satisfactory. NIRS accuracy was lower for the other parameters. Nevertheless, RPD (ratio standard deviation of concentration/SEP) and RER (ratio concentration range/SEP ratio) show that NIRS might be used for the sample screening on nitrogen, potassium, sodium, and glucose content.  相似文献   

12.
Near-infrared reflectance spectroscopy (NIRS) was used to develop calibration curves for determining the fat acidity of whole-kernel and ground rough rice with 13% moisture content at 25°C. Partial-leastsquares regression (PLSR) uses the optimal calibration curve for wholekernel rough rice to measure the coefficient of determination (r2) of validation and standard error of prediction (SEP) of 0.87 and 0.83 mg of KOH/100 g of dry matter, respectively. However, the optimal calibration curve for ground rough rice has a higher r2 of validation and lower SEP of 0.94 and 0.73 mg of KOH/100 g of dry matter, respectively. From 10 to 40°C, the temperature effect causes an increase of 0.24 mg of KOH/100 g of dry matter/°C in the predicted fat acidity of whole-kernel rough rice.  相似文献   

13.
Near-infrared reflectance spectroscopy (NIRS) was used as a rapid and nondestructive method to determine the oil content and fatty acid composition in intact seeds of perilla [Perilla frutescens var. japonica (Hassk.) Hara] germplasms in Korea. A total of 397 samples (about 2 g of intact seeds) were scanned in the reflectance mode of a scanning monochromator, and the reference values for the oil content and fatty acid composition were measured by gravimetric method and gas-liquid chromatography, respectively. Calibration equations for oil and individual fatty acids were developed using modified partial least-squares regression with internal cross validation (n = 297). The equations for oil and oleic and linolenic acid had lower standard errors of cross-validation (SECV), higher R2 (coefficient of determination in calibration), and higher ratio of unexplained variance divided by variance (1-VR) values than those for palmitic, stearic, and linoleic acid. Prediction of an external validation set (n = 100) showed significant correlation between reference values and NIRS estimated values based on the standard error of prediction (SEP), r2 (coefficient of determination in prediction), and the ratio of standard deviation (SD) of reference data to SEP. The models for oil content and major fatty acids, oleic and linolenic acid, had relatively higher values of SD/SEP(C) and r2 (more than 3.0 and 0.9, respectively), thereby characterizing those equations as having good quantitative information, whereas those of palmitic, stearic, and linoleic acid had lower values (below 2.0 and 0.7, respectively), unsuitable for screening purposes. The results indicated that NIRS could be used to rapidly determine oil content and fatty acid composition (oleic and linolenic acid) in perilla seeds in the breeding programs for development of high-quality perilla oil.  相似文献   

14.
Hydrogen cyanide (HCN) is a toxic chemical that can potentially cause mild to severe reactions in animals when grazing forage sorghum. Developing technologies to monitor the level of HCN in the growing crop would benefit graziers, so that they can move cattle into paddocks with acceptable levels of HCN. In this study, we developed near-infrared spectroscopy (NIRS) calibrations to estimate HCN in forage sorghum and hay. The full spectral NIRS range (400-2498 nm) was used as well as specific spectral ranges within the full spectral range, i.e., visible (400-750 nm), shortwave (800-1100 nm) and near-infrared (NIR) (1100-2498 nm). Using the full spectrum approach and partial least-squares (PLS), the calibration produced a coefficient of determination (R(2)) = 0.838 and standard error of cross-validation (SECV) = 0.040%, while the validation set had a R(2) = 0.824 with a low standard error of prediction (SEP = 0.047%). When using a multiple linear regression (MLR) approach, the best model (NIR spectra) produced a R(2) = 0.847 and standard error of calibration (SEC) = 0.050% and a R(2) = 0.829 and SEP = 0.057% for the validation set. The MLR models built from these spectral regions all used nine wavelengths. Two specific wavelengths 2034 and 2458 nm were of interest, with the former associated with C═O carbonyl stretch and the latter associated with C-N-C stretching. The most accurate PLS and MLR models produced a ratio of standard error of prediction to standard deviation of 3.4 and 3.0, respectively, suggesting that the calibrations could be used for screening breeding material. The results indicated that it should be feasible to develop calibrations using PLS or MLR models for a number of users, including breeding programs to screen for genotypes with low HCN, as well as graziers to monitor crop status to help with grazing efficiency.  相似文献   

15.
Phytochemicals such as phenolics and flavonoids, which are present in rice grains, are associated with reduced risk of developing chronic diseases such as cardiovascular disease, type 2 diabetes, and some cancers. The phenolic and flavonoid compounds in rice grain also contribute to the antioxidant activity. Biofortification of rice grain by conventional breeding is a way to improve nutritional quality so as to combat nutritional deficiency. Since wet chemistry measurement of phenolic and flavonoid contents and antioxidant activity are time-consuming and expensive, a rapid and nondestructive predictive method based on near-infrared spectroscopy (NIRS) would be valuable to measure these nutritional quality parameters. In the present study, calibration models for measurement of phenolic and flavonoid contents and antioxidant capacity were developed using principal component analysis (PCA), partial least-squares regression (PLS), and modified partial least-squares regression (mPLS) methods with the spectra of the dehulled grain (brown rice). The results showed that NIRS could effectively predict the total phenolic contents and antioxidant capacity by PLS and mPLS methods. The standard errors of prediction (SEP) were 47.1 and 45.9 mg gallic acid equivalent (GAE) for phenolic content, and the coefficients of determination ( r (2)) were 0.849 and 0.864 by PLS and mPLS methods, respectively. Both PLS and mPLS methods gave similarly accurate performance for prediction of antioxidant capacity with SEP of 0.28 mM Trolox equivalent antioxidant capacity (TEAC) and r (2) of 0.82. However, the NIRS models were not successful for flavonoid content with the three methods ( r (2) < 0.4). The models reported here are usable for routine screening of a large number of samples in early generation screening in breeding programs.  相似文献   

16.
Abstract

An evaluation of the performance of near‐infrared reflectance spectroscopy (NIRS) in the analysis of nitrogen (N) concentration in different rapeseed (Brassica napus L.) tissues was made. A total of 228 samples from an N‐efficiency study corresponding to leaves and stems at flowering, fallen leaves, mature stems, and mature pod walls were oven dried, ground, and then analyzed by NIRS. The N concentration was determined by Dumas combustion. Two different calibration strategies were followed: (i) separate calibration equations were developed for each type of tissue, resulting in r2 above 0.95 in crossvalidation for all tissues with the ratio of the standard error of crossvalidation (SECV) to the standard deviation of the population (SD) ranging from 0.10 to 0.22, and (ii) a NIRS calibration equation was developed from a set integrating 149 samples from the five groups of tissues. External . validation with a set containing 79 further samples from all the groups resulted in an r2 of 0.99 and a ratio of the standard error of performance (SEP) to the SD of 0.08. External validation for each group separately resulted in r2 from 0.91 to 0.99 and SEP/SD from 0.10 to 0.27. It was concluded that a universal NIRS calibration equation integrating samples from all the types of tissues is an adequate approach for the accurate analysis of N concentration in rapeseed. Based on our results, the NIRS technique can reliably replace the Kjeldahl or Dumas methods to determine the N concentration in investigations of the N efficiency in rapeseed.  相似文献   

17.
玉米非淀粉组分是可再生的生物质资源,为实现玉米皮渣中纤维素及半纤维含量的快速检测,该研究以偏最小二乘法(PLS)建立数学模型,探讨一阶导数及二阶导数平滑等预处理对建模的影响,建立玉米皮渣中纤维素及半纤维素近红外分析模型.研究结果表明,纤维素模型的定标集和验证集相关系数为0.9806和0.9799,定标集标准偏差(SEE...  相似文献   

18.
Near-infrared spectroscopy (NIRS) is a well-established technique for determining the components of foods. Sample preparation for NIRS is easy, making it suitable for breeding and/or quality evaluation, for which a large number of samples should be analyzed. We aimed to assess the feasibility of NIRS to estimate parameters that seem to influence consumers' perception of the seed coat of common beans: dietary fiber (DF), uronic acids (UA), ashes, calcium, and magnesium. We used reference methods to analyze ground seed coats of 90 common bean samples with a wide range of genetic variability and cultivated at many locations. We registered the NIR spectra on intact beans and ground seed coat samples. We derived partial least-squares (PLS) regression equations from a set of calibration samples and tested their predictive power in an external validation set. For intact beans, only RER values for ashes and calcium are good enough for very rough screening. For ground seed coat samples, the RPD and RER values for ashes (3.49 and 14.09, respectively) and calcium (3.57 and 12.70, respectively) are good enough for screening. RPD and RER values for DF (2.60 and 9.15, respectively) and RER values for magnesium (6.57) also enable rough screening. A poorer correlation was achieved for UA. We conclude that NIRS can help in common bean breeding research and quality evaluation.  相似文献   

19.
The purpose of this study was to develop highly accurate regression models with texture parameters of cooked milled rice grains for predicting pasting properties in terms of quality index of rice flour. Two methods were adopted as the texture measurement to acquire predictors for the models. In the calibration set, all the multiple regression models by a single‐grain method exhibited a higher R2 than those by a three‐grain method. Each of the former models also showed a lower SEP and a higher RPD in the validation set. The prediction performance was best for consistency (RPD = 2.4). The single‐grain method was more advantageous for the pasting prediction. These results suggest that the models based on grain texture could predict rice flour quality.  相似文献   

20.
Quality protein maize (QPM) has approximately twice the tryptophan (Trp) and lysine (Lys) concentrations in protein compared to normal maize. Because several genetic systems control the protein quality of QPM, it is essential to regularly monitor Trp and/or Lys in breeding programs. Our objective was to examine the potential of near-infrared reflectance spectroscopy (NIRS) to enhance the efficiency of QPM research efforts by partially replacing more expensive and time-consuming wet chemistry analysis. More than 276 maize samples were used to develop NIRS models for protein content (PC), Trp, and Lys. The standard error of prediction (SEP) for the calibration and the coefficient of determination for validation (R(2)(v)) were 0.26 and 0.96 for PC, 0.005 and 0.85 for Trp, and 0.02 and 0.75 for Lys. When the NIRS models were used to evaluate 266 S2 lines from five QPM breeding populations, the coefficients of determination between NIRS and the chemical data were 0.94, 0.76, and 0.80 for PC, Trp, and Lys, respectively. Therefore, the NIRS models can be used to support the QPM breeding efforts.  相似文献   

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